A single latent video flow-matching prior, with posterior guidance, performs super-resolution, multimodal data fusion, filtering/smoothing, and observation-to-forecast for the global atmosphere using real station observations.
Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems
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abstract
In the absence of high-resolution samples, super-resolution of sparse observations on dynamical systems is a challenging problem with wide-reaching applications in experimental settings. We showcase the application of physics-informed convolutional neural networks for super-resolution of sparse observations on grids. Results are shown for the chaotic-turbulent Kolmogorov flow, demonstrating the potential of this method for resolving finer scales of turbulence when compared with classic interpolation methods, and thus effectively reconstructing missing physics.
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Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching
A single latent video flow-matching prior, with posterior guidance, performs super-resolution, multimodal data fusion, filtering/smoothing, and observation-to-forecast for the global atmosphere using real station observations.